mirror of https://github.com/coqui-ai/TTS.git
Test updates
Eren Golge
parent
c8a552e627
commit
4681f935b4
11
.compute
11
.compute
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@ -1,10 +1,5 @@
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#!/bin/bash
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source ../tmp/venv/bin/activate
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# ls /snakepit/jobs/1023/keep/
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# source /snakepit/jobs/1023/keep/venv/bin/activate
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# source /snakepit/jobs/1047/keep/venv/bin/activate
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# python extract_feats.py --data_path /snakepit/shared/data/keithito/LJSpeech-1.1/wavs --out_path /snakepit/shared/data/keithito/LJSpeech-1.1/loader_data/ --config config.json --num_proc 32
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# python train.py --config_path config_kusal.json --debug true
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python train.py --config_path config.json --debug true
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# python -m cProfile -o profile.cprof train.py --config_path config.json --debug true
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# nvidia-smi
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# python extract_features.py --data_path ${DATA_ROOT}/shared/data/keithito/LJSpeech-1.1/ --cache_path ~/tts_cache/ --config config.json --num_proc 12 --dataset ljspeech --meta_file metadata.csv --val_split 1000 --process_audio true
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# python train.py --config_path config.json --data_path ~/tts_cache/ --debug true
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python train.py --config_path config.json --data_path ${DATA_ROOT}/shared/data/keithito/LJSpeech-1.1/ --debug true
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2
.install
2
.install
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@ -1,4 +1,4 @@
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#!/bin/bash
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virtualenv -p python3 ../tmp/venv
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source ../tmp/venv/bin/activate
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python setup.py develop
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python setup.py develop
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108
extract_feats.py
108
extract_feats.py
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@ -1,108 +0,0 @@
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'''
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Extract spectrograms and save them to file for training
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'''
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import os
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import sys
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import time
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import glob
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import argparse
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import librosa
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import numpy as np
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import tqdm
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from utils.generic_utils import load_config
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from multiprocessing import Pool
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--data_path', type=str, help='Data folder.')
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parser.add_argument('--out_path', type=str, help='Output folder.')
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parser.add_argument(
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'--config', type=str, help='conf.json file for run settings.')
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parser.add_argument(
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"--num_proc", type=int, default=8, help="number of processes.")
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parser.add_argument(
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"--trim_silence",
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type=bool,
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default=False,
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help="trim silence in the voice clip.")
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args = parser.parse_args()
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DATA_PATH = args.data_path
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OUT_PATH = args.out_path
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CONFIG = load_config(args.config)
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print(" > Input path: ", DATA_PATH)
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print(" > Output path: ", OUT_PATH)
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audio = importlib.import_module('utils.' + c.audio_processor)
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AudioProcessor = getattr(audio, 'AudioProcessor')
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ap = AudioProcessor(
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sample_rate=CONFIG.sample_rate,
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num_mels=CONFIG.num_mels,
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min_level_db=CONFIG.min_level_db,
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frame_shift_ms=CONFIG.frame_shift_ms,
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frame_length_ms=CONFIG.frame_length_ms,
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ref_level_db=CONFIG.ref_level_db,
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num_freq=CONFIG.num_freq,
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power=CONFIG.power,
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preemphasis=CONFIG.preemphasis,
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min_mel_freq=CONFIG.min_mel_freq,
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max_mel_freq=CONFIG.max_mel_freq)
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def trim_silence(self, wav):
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margin = int(CONFIG.sample_rate * 0.1)
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wav = wav[margin:-margin]
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return librosa.effects.trim(
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wav, top_db=40, frame_length=1024, hop_length=256)[0]
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def extract_mel(file_path):
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# x, fs = sf.read(file_path)
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x, fs = librosa.load(file_path, CONFIG.sample_rate)
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if args.trim_silence:
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x = trim_silence(x)
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mel = ap.melspectrogram(x.astype('float32')).astype('float32')
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linear = ap.spectrogram(x.astype('float32')).astype('float32')
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file_name = os.path.basename(file_path).replace(".wav", "")
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mel_file = file_name + ".mel"
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linear_file = file_name + ".linear"
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np.save(os.path.join(OUT_PATH, mel_file), mel, allow_pickle=False)
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np.save(
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os.path.join(OUT_PATH, linear_file), linear, allow_pickle=False)
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mel_len = mel.shape[1]
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linear_len = linear.shape[1]
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wav_len = x.shape[0]
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print(" > " + file_path, flush=True)
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return file_path, mel_file, linear_file, str(wav_len), str(
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mel_len), str(linear_len)
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glob_path = os.path.join(DATA_PATH, "*.wav")
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print(" > Reading wav: {}".format(glob_path))
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file_names = glob.glob(glob_path, recursive=True)
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if __name__ == "__main__":
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print(" > Number of files: %i" % (len(file_names)))
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if not os.path.exists(OUT_PATH):
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os.makedirs(OUT_PATH)
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print(" > A new folder created at {}".format(OUT_PATH))
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r = []
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if args.num_proc > 1:
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print(" > Using {} processes.".format(args.num_proc))
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with Pool(args.num_proc) as p:
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r = list(
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tqdm.tqdm(
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p.imap(extract_mel, file_names),
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total=len(file_names)))
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# r = list(p.imap(extract_mel, file_names))
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else:
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print(" > Using single process run.")
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for file_name in file_names:
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print(" > ", file_name)
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r.append(extract_mel(file_name))
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file_path = os.path.join(OUT_PATH, "meta_fftnet.csv")
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file = open(file_path, "w")
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for line in r:
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line = ", ".join(line)
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file.write(line + '\n')
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file.close()
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@ -15,6 +15,13 @@ os.makedirs(OUTPATH, exist_ok=True)
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c = load_config(os.path.join(file_path, 'test_config.json'))
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ok_ljspeech = os.path.exists(c.data_path)
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DATA_EXIST = True
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CACHE_EXIST = True
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if not os.path.exists(c.data_path_cache):
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CACHE_EXIST = False
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if not os.path.exists(c.data_path):
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DATA_EXIST = False
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class TestTTSDataset(unittest.TestCase):
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def __init__(self, *args, **kwargs):
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